Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	14.0	22.520885
daal4py_KNeighborsClassifier	0.0	3.0	27.473946
KNeighborsClassifier_kd_tree	0.0	6.0	43.563386
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	48.883594
KMeans_tall	0.0	1.0	40.918294
daal4py_KMeans_tall	0.0	1.0	19.199566
KMeans_short	0.0	0.0	19.959288
daal4py_KMeans_short	0.0	0.0	9.932689
LogisticRegression	0.0	1.0	4.698853
daal4py_LogisticRegression	0.0	0.0	57.526663
Ridge	0.0	0.0	26.275623
daal4py_Ridge	0.0	0.0	6.973181
total	0.0	32.0	28.002008

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.133	0.007	1000000	1000000	100	brute	-1	1	6.033	NaN	0.977	0.986	0.483	0.002	0.275	0.014	See
1	KNeighborsClassifier	predict	25.800	0.443	1000000	1000	100	brute	-1	1	0.000	0.026	0.977	0.986	2.017	0.012	12.789	0.232	See
2	KNeighborsClassifier	predict	0.171	0.017	1000000	1	100	brute	-1	1	0.005	0.000	0.977	0.986	0.086	0.001	1.997	0.201	See
3	KNeighborsClassifier	fit	0.138	0.003	1000000	1000000	100	brute	-1	5	5.779	NaN	0.977	0.986	0.493	0.004	0.281	0.006	See
4	KNeighborsClassifier	predict	36.624	0.000	1000000	1000	100	brute	-1	5	0.000	0.037	0.977	0.986	2.013	0.018	18.189	0.166	See
5	KNeighborsClassifier	predict	0.188	0.019	1000000	1	100	brute	-1	5	0.004	0.000	0.977	0.986	0.087	0.001	2.178	0.221	See
6	KNeighborsClassifier	fit	0.131	0.004	1000000	1000000	100	brute	-1	100	6.109	NaN	0.977	0.986	0.491	0.002	0.267	0.007	See
7	KNeighborsClassifier	predict	36.651	0.000	1000000	1000	100	brute	-1	100	0.000	0.037	0.977	0.986	2.073	0.014	17.683	0.123	See
8	KNeighborsClassifier	predict	0.173	0.012	1000000	1	100	brute	-1	100	0.005	0.000	0.977	0.986	0.085	0.001	2.019	0.140	See
9	KNeighborsClassifier	fit	0.126	0.001	1000000	1000000	100	brute	1	1	6.369	NaN	0.977	0.986	0.493	0.004	0.255	0.003	See
10	KNeighborsClassifier	predict	13.256	0.024	1000000	1000	100	brute	1	1	0.000	0.013	0.977	0.986	1.995	0.018	6.643	0.062	See
11	KNeighborsClassifier	predict	0.182	0.001	1000000	1	100	brute	1	1	0.004	0.000	0.977	0.986	0.085	0.001	2.138	0.026	See
12	KNeighborsClassifier	fit	0.127	0.001	1000000	1000000	100	brute	1	5	6.324	NaN	0.977	0.986	0.491	0.001	0.257	0.001	See
13	KNeighborsClassifier	predict	24.833	0.023	1000000	1000	100	brute	1	5	0.000	0.025	0.977	0.986	1.997	0.010	12.437	0.061	See
14	KNeighborsClassifier	predict	0.193	0.003	1000000	1	100	brute	1	5	0.004	0.000	0.977	0.986	0.086	0.001	2.255	0.038	See
15	KNeighborsClassifier	fit	0.126	0.001	1000000	1000000	100	brute	1	100	6.341	NaN	0.977	0.986	0.494	0.004	0.255	0.003	See
16	KNeighborsClassifier	predict	24.844	0.049	1000000	1000	100	brute	1	100	0.000	0.025	0.977	0.986	2.076	0.016	11.970	0.094	See
17	KNeighborsClassifier	predict	0.194	0.004	1000000	1	100	brute	1	100	0.004	0.000	0.977	0.986	0.087	0.002	2.235	0.063	See
18	KNeighborsClassifier	fit	0.061	0.001	1000000	1000000	2	brute	-1	1	0.263	NaN	0.977	0.986	0.110	0.004	0.554	0.019	See
19	KNeighborsClassifier	predict	22.414	0.057	1000000	1000	2	brute	-1	1	0.000	0.022	0.977	0.986	0.314	0.005	71.297	1.246	See
20	KNeighborsClassifier	predict	0.022	0.002	1000000	1	2	brute	-1	1	0.001	0.000	0.977	0.986	0.006	0.001	3.423	0.458	See
21	KNeighborsClassifier	fit	0.062	0.000	1000000	1000000	2	brute	-1	5	0.259	NaN	0.977	0.986	0.110	0.004	0.561	0.020	See
22	KNeighborsClassifier	predict	33.966	0.000	1000000	1000	2	brute	-1	5	0.000	0.034	0.977	0.986	0.316	0.006	107.416	2.071	See
23	KNeighborsClassifier	predict	0.036	0.002	1000000	1	2	brute	-1	5	0.000	0.000	0.977	0.986	0.006	0.001	5.613	0.801	See
24	KNeighborsClassifier	fit	0.064	0.003	1000000	1000000	2	brute	-1	100	0.252	NaN	0.977	0.986	0.108	0.001	0.587	0.027	See
25	KNeighborsClassifier	predict	34.338	0.000	1000000	1000	2	brute	-1	100	0.000	0.034	0.977	0.986	0.367	0.002	93.634	0.469	See
26	KNeighborsClassifier	predict	0.036	0.002	1000000	1	2	brute	-1	100	0.000	0.000	0.977	0.986	0.006	0.000	5.785	0.393	See
27	KNeighborsClassifier	fit	0.069	0.005	1000000	1000000	2	brute	1	1	0.231	NaN	0.977	0.986	0.110	0.002	0.629	0.046	See
28	KNeighborsClassifier	predict	10.460	0.002	1000000	1000	2	brute	1	1	0.000	0.010	0.977	0.986	0.311	0.002	33.582	0.265	See
29	KNeighborsClassifier	predict	0.016	0.001	1000000	1	2	brute	1	1	0.001	0.000	0.977	0.986	0.006	0.001	2.468	0.222	See
30	KNeighborsClassifier	fit	0.062	0.000	1000000	1000000	2	brute	1	5	0.260	NaN	0.977	0.986	0.109	0.001	0.564	0.006	See
31	KNeighborsClassifier	predict	22.354	0.038	1000000	1000	2	brute	1	5	0.000	0.022	0.977	0.986	0.311	0.001	71.783	0.361	See
32	KNeighborsClassifier	predict	0.030	0.001	1000000	1	2	brute	1	5	0.001	0.000	0.977	0.986	0.006	0.001	4.676	0.775	See
33	KNeighborsClassifier	fit	0.061	0.000	1000000	1000000	2	brute	1	100	0.261	NaN	0.977	0.986	0.109	0.001	0.560	0.006	See
34	KNeighborsClassifier	predict	22.426	0.009	1000000	1000	2	brute	1	100	0.000	0.022	0.977	0.986	0.371	0.003	60.420	0.468	See
35	KNeighborsClassifier	predict	0.030	0.001	1000000	1	2	brute	1	100	0.001	0.000	0.977	0.986	0.008	0.001	3.892	0.568	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	throughput	latency	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.316	0.025	1000000	1000000	10	kd_tree	-1	1	0.024	NaN	0.989	0.987	0.746	0.008	4.444	0.056	See
1	KNeighborsClassifier_kd_tree	predict	0.474	0.006	1000000	1000	10	kd_tree	-1	1	0.000	0.000	0.989	0.987	0.101	0.003	4.703	0.158	See
2	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	1	0.021	0.000	0.989	0.987	0.000	0.000	13.855	6.411	See
3	KNeighborsClassifier_kd_tree	fit	3.389	0.053	1000000	1000000	10	kd_tree	-1	5	0.024	NaN	0.989	0.987	0.774	0.007	4.376	0.080	See
4	KNeighborsClassifier_kd_tree	predict	0.915	0.009	1000000	1000	10	kd_tree	-1	5	0.000	0.001	0.989	0.987	0.185	0.003	4.944	0.101	See
5	KNeighborsClassifier_kd_tree	predict	0.005	0.001	1000000	1	10	kd_tree	-1	5	0.017	0.000	0.989	0.987	0.000	0.000	12.494	5.894	See
6	KNeighborsClassifier_kd_tree	fit	3.348	0.069	1000000	1000000	10	kd_tree	-1	100	0.024	NaN	0.989	0.987	0.749	0.011	4.472	0.115	See
7	KNeighborsClassifier_kd_tree	predict	2.918	0.022	1000000	1000	10	kd_tree	-1	100	0.000	0.003	0.989	0.987	0.565	0.008	5.162	0.079	See
8	KNeighborsClassifier_kd_tree	predict	0.009	0.001	1000000	1	10	kd_tree	-1	100	0.009	0.000	0.989	0.987	0.001	0.000	11.305	4.351	See
9	KNeighborsClassifier_kd_tree	fit	3.281	0.019	1000000	1000000	10	kd_tree	1	1	0.024	NaN	0.989	0.987	0.788	0.007	4.166	0.046	See
10	KNeighborsClassifier_kd_tree	predict	0.775	0.006	1000000	1000	10	kd_tree	1	1	0.000	0.001	0.989	0.987	0.100	0.005	7.766	0.400	See
11	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	1	1	0.050	0.000	0.989	0.987	0.000	0.000	6.062	2.652	See
12	KNeighborsClassifier_kd_tree	fit	3.294	0.041	1000000	1000000	10	kd_tree	1	5	0.024	NaN	0.989	0.987	0.765	0.016	4.305	0.107	See
13	KNeighborsClassifier_kd_tree	predict	1.516	0.010	1000000	1000	10	kd_tree	1	5	0.000	0.002	0.989	0.987	0.181	0.003	8.352	0.150	See
14	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	1	5	0.035	0.000	0.989	0.987	0.000	0.000	6.713	3.118	See
15	KNeighborsClassifier_kd_tree	fit	3.305	0.048	1000000	1000000	10	kd_tree	1	100	0.024	NaN	0.989	0.987	0.785	0.009	4.212	0.078	See
16	KNeighborsClassifier_kd_tree	predict	4.977	0.031	1000000	1000	10	kd_tree	1	100	0.000	0.005	0.989	0.987	0.562	0.005	8.854	0.094	See
17	KNeighborsClassifier_kd_tree	predict	0.007	0.001	1000000	1	10	kd_tree	1	100	0.011	0.000	0.989	0.987	0.001	0.000	9.374	3.678	See
18	KNeighborsClassifier_kd_tree	fit	0.883	0.010	1000000	1000000	2	kd_tree	-1	1	0.018	NaN	0.989	0.987	0.518	0.010	1.704	0.038	See
19	KNeighborsClassifier_kd_tree	predict	0.029	0.001	1000000	1000	2	kd_tree	-1	1	0.001	0.000	0.989	0.987	0.001	0.000	25.862	9.070	See
20	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	1	0.006	0.000	0.989	0.987	0.000	0.000	20.724	15.480	See
21	KNeighborsClassifier_kd_tree	fit	0.885	0.011	1000000	1000000	2	kd_tree	-1	5	0.018	NaN	0.989	0.987	0.517	0.010	1.713	0.038	See
22	KNeighborsClassifier_kd_tree	predict	0.031	0.001	1000000	1000	2	kd_tree	-1	5	0.001	0.000	0.989	0.987	0.002	0.001	19.478	10.136	See
23	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	5	0.006	0.000	0.989	0.987	0.000	0.000	21.138	16.257	See
24	KNeighborsClassifier_kd_tree	fit	0.894	0.019	1000000	1000000	2	kd_tree	-1	100	0.018	NaN	0.989	0.987	0.512	0.006	1.747	0.042	See
25	KNeighborsClassifier_kd_tree	predict	0.051	0.001	1000000	1000	2	kd_tree	-1	100	0.000	0.000	0.989	0.987	0.008	0.001	6.668	0.680	See
26	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	100	0.006	0.000	0.989	0.987	0.000	0.000	20.610	14.752	See
27	KNeighborsClassifier_kd_tree	fit	0.917	0.023	1000000	1000000	2	kd_tree	1	1	0.017	NaN	0.989	0.987	0.521	0.007	1.761	0.050	See
28	KNeighborsClassifier_kd_tree	predict	0.026	0.000	1000000	1000	2	kd_tree	1	1	0.001	0.000	0.989	0.987	0.001	0.001	24.497	14.492	See
29	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	0.023	0.000	0.989	0.987	0.000	0.000	3.890	2.680	See
30	KNeighborsClassifier_kd_tree	fit	0.877	0.017	1000000	1000000	2	kd_tree	1	5	0.018	NaN	0.989	0.987	0.524	0.006	1.673	0.038	See
31	KNeighborsClassifier_kd_tree	predict	0.028	0.001	1000000	1000	2	kd_tree	1	5	0.001	0.000	0.989	0.987	0.001	0.001	19.306	8.398	See
32	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	0.022	0.000	0.989	0.987	0.000	0.000	5.483	3.841	See
33	KNeighborsClassifier_kd_tree	fit	0.891	0.019	1000000	1000000	2	kd_tree	1	100	0.018	NaN	0.989	0.987	0.529	0.015	1.686	0.061	See
34	KNeighborsClassifier_kd_tree	predict	0.056	0.001	1000000	1000	2	kd_tree	1	100	0.000	0.000	0.989	0.987	0.008	0.001	7.192	1.331	See
35	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	0.022	0.000	0.989	0.987	0.000	0.000	5.624	3.889	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.653	0.016	1000000	1000000	2	full	k-means++	30	3	1	0.0	30	0.735	NaN	0.002	30	0.001	0.497	0.021	1.313	0.064	See
1	KMeans_tall	predict	0.002	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	30	0.011	0.0	0.002	30	0.001	0.000	0.000	6.687	4.234	See
2	KMeans_tall	predict	0.001	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	30	0.011	0.0	0.002	30	0.001	0.000	0.000	6.765	3.464	See
3	KMeans_tall	fit	0.564	0.007	1000000	1000000	2	full	random	30	3	1	0.0	30	0.851	NaN	0.002	30	0.001	0.443	0.013	1.275	0.040	See
4	KMeans_tall	predict	0.001	0.000	1000000	1000	2	full	random	30	3	1	0.0	30	0.011	0.0	0.002	30	0.001	0.000	0.000	7.402	3.741	See
5	KMeans_tall	predict	0.001	0.000	1000000	1	2	full	random	30	3	1	0.0	30	0.011	0.0	0.002	30	0.001	0.000	0.000	10.791	7.068	See
6	KMeans_tall	fit	6.400	0.108	1000000	1000000	100	full	k-means++	30	3	1	0.0	30	3.750	NaN	0.002	30	0.001	3.089	0.027	2.072	0.039	See
7	KMeans_tall	predict	0.002	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	30	0.440	0.0	0.002	30	0.001	0.000	0.000	5.479	2.820	See
8	KMeans_tall	predict	0.002	0.001	1000000	1	100	full	k-means++	30	3	1	0.0	30	0.478	0.0	0.002	30	0.001	0.000	0.000	8.856	6.708	See
9	KMeans_tall	fit	6.051	0.020	1000000	1000000	100	full	random	30	3	1	0.0	30	3.966	NaN	0.002	30	0.001	2.940	0.049	2.058	0.035	See
10	KMeans_tall	predict	0.002	0.001	1000000	1000	100	full	random	30	3	1	0.0	30	0.356	0.0	0.002	30	0.001	0.000	0.000	7.762	4.380	See
11	KMeans_tall	predict	0.001	0.000	1000000	1	100	full	random	30	3	1	0.0	30	0.546	0.0	0.002	30	0.001	0.000	0.000	7.652	4.840	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	throughput	latency	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.300	0.015	10000	10000	2	full	k-means++	30	300	1	0.0	30	0.016	NaN	0.0	30	0.007	0.111	0.004	2.704	0.172	See
1	KMeans_short	predict	0.002	0.001	10000	1000	2	full	k-means++	30	300	1	0.0	30	0.007	0.0	0.0	30	0.007	0.001	0.000	2.769	1.225	See
2	KMeans_short	predict	0.001	0.000	10000	1	2	full	k-means++	30	300	1	0.0	30	0.011	0.0	0.0	30	0.007	0.000	0.000	10.197	6.659	See
3	KMeans_short	fit	0.133	0.005	10000	10000	2	full	random	30	300	1	0.0	30	0.036	NaN	0.0	30	0.007	0.048	0.001	2.760	0.124	See
4	KMeans_short	predict	0.002	0.001	10000	1000	2	full	random	30	300	1	0.0	30	0.007	0.0	0.0	30	0.007	0.001	0.000	2.980	1.014	See
5	KMeans_short	predict	0.002	0.000	10000	1	2	full	random	30	300	1	0.0	30	0.010	0.0	0.0	30	0.007	0.000	0.000	10.341	6.719	See
6	KMeans_short	fit	0.682	0.021	10000	10000	100	full	k-means++	30	300	1	0.0	22	0.258	NaN	0.0	19	0.007	0.402	0.022	1.697	0.108	See
7	KMeans_short	predict	0.003	0.001	10000	1000	100	full	k-means++	30	300	1	0.0	22	0.262	0.0	0.0	19	0.007	0.001	0.000	2.217	0.584	See
8	KMeans_short	predict	0.002	0.000	10000	1	100	full	k-means++	30	300	1	0.0	22	0.517	0.0	0.0	19	0.007	0.000	0.000	7.143	3.754	See
9	KMeans_short	fit	0.292	0.048	10000	10000	100	full	random	30	300	1	0.0	30	0.822	NaN	0.0	25	0.007	0.184	0.024	1.589	0.333	See
10	KMeans_short	predict	0.003	0.000	10000	1000	100	full	random	30	300	1	0.0	30	0.281	0.0	0.0	25	0.007	0.001	0.000	2.107	0.552	See
11	KMeans_short	predict	0.002	0.000	10000	1	100	full	random	30	300	1	0.0	30	0.518	0.0	0.0	25	0.007	0.000	0.000	8.544	4.798	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	throughput	latency	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.678	0.023	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	[-0.10103401]	NaN	0.26	11.639	0.021	1.003	0.003	See
1	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	2.1584973835343684	0.0	0.26	0.000	0.000	0.868	0.403	See
2	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	9.857801216392623	0.0	0.26	0.000	0.000	0.421	0.363	See
3	LogisticRegression	fit	0.776	0.009	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	[2.68003413]	NaN	0.26	0.771	0.003	1.006	0.013	See
4	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	4.452439789320023	0.0	0.26	0.003	0.000	0.535	0.103	See
5	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	77.96890987503261	0.0	0.26	0.001	0.000	0.133	0.089	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	throughput	latency	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	0.192	0.005	1000	1000	10000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	0.416	NaN	1.0	0.196	0.002	0.982	0.028	See
1	Ridge	predict	0.010	0.000	1000	1000	10000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	7.880	0.0	1.0	0.017	0.000	0.602	0.026	See
2	Ridge	predict	0.000	0.000	1000	1	10000	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	1053.966	0.0	1.0	0.000	0.000	0.643	0.608	See
3	Ridge	fit	1.226	0.053	1000000	1000000	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	0.652	NaN	1.0	0.243	0.004	5.043	0.233	See
4	Ridge	predict	0.000	0.000	1000000	1000	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	3.193	0.0	1.0	0.000	0.000	0.737	0.829	See
5	Ridge	predict	0.000	0.000	1000000	1	100	1.0	True	True	NaN	deprecated	NaN	auto	0.001	NaN	11.983	0.0	1.0	0.000	0.000	0.383	0.423	See

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.2",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}